Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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An Analysis on the Properties of Features against Various Distortions in Deep Neural Networks

  • Kang, Jung Heum (Department of Computer Science and Engineering, Kyung Hee Univ.) ;
  • Jeong, Hye Won (Department of Computer Science and Engineering, Kyung Hee Univ.) ;
  • Choi, Chang Kyun (Department of Computer Science and Engineering, Kyung Hee Univ.) ;
  • Ali, Muhammad Salman (Department of Computer Science and Engineering, Kyung Hee Univ.) ;
  • Bae, Sung-Ho (Department of Computer Science and Engineering, Kyung Hee Univ.) ;
  • Kim, Hui Yong (Department of Computer Science and Engineering, Kyung Hee Univ.)
  • Received : 2021.10.25
  • Accepted : 2021.11.29
  • Published : 2021.12.20
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Abstract

Deploying deep neural network model training performs remarkable performance in the fields of Object detection and Instance segmentation. To train these models, features are first extracted from the input image using a backbone network. The extracted features can be reused by various tasks. Research has been actively conducted to serve various tasks by using these learned features. In this process, standardization discussions about encoding, decoding, and transmission methods are proceeding actively. In this scenario, it is necessary to analyze the response characteristics of features against various distortions that may occur in the data transmission or data compression process. In this paper, experiment was conducted to inject various distortions into the feature in the object recognition task. And analyze the mAP (mean Average Precision) metric between the predicted value output from the neural network and the target value as the intensity of various distortions was increased. Experiments have shown that features are more robust to distortion than images. And this points out that using the feature as transmission means can prevent the loss of information against the various distortions during data transmission and compression process.

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Acknowledgement

This work was supported by Institute of Information Communications Technology Planning Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00011, Video Coding for Machine). This research was a result of a study on the NIPA.

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